Animal Vocalizations Research Articles

Persistency of horse habituation to distracting sounds

Habituation indicates a decrement in response to a specific stimulus by repeated exposure to that stimulus. Responding to or ignoring sound stimuli, which are common background distractors, is important for the undisturbed functioning of the organism, efficient animal management and safe handling. We habituated 20 warmblood horses to a set of distracting sounds and assessed the possibility of reversing this process through simple modifications in sound playback. The study involved three stages: habituation (Stage 1) and two trials of dishabituation (Stage 2, 3). In Stage 1, five different sounds of animals, each on a separate day, were repeatedly (every 20 minutes (min)) played during a stay on an experimental paddock. The sounds were selected out of 40 sounds of different origin based on their distracting effect on the horses (e.g., interruption of previous activity, increase in emotional arousal) during the first exposure to them three months earlier. In Stage 2, four new and unknown sounds (futuristic characteristics) were additionally introduced to the set of habituated sounds, whereas in Stage 3, habituated sounds (from Stage 1) were played from an altered location. Behavioral (e.g., disruption of feeding, duration of feeding, locomotion and standing alert) and cardiac (heart rate and heart rate variability) variables were assessed. Generally, horses quickly habituated to the initially distracting sounds, but great variability was observed in the number of needed playbacks (1–7) for each horse. After completion of Stage 1, the horses responded with an increase in vigilance, locomotion, refeeding frequency and a 1-minute increase in mean heart rate to novel but not to known sounds. None of the modifications resulted in a recovery of previously habituated reactions. After introduction of playback modifications, only maximum heart rate (in Stage 2, Stage 3) and time of standing alert (in Stage 3) increased after re-exposure to sounds horses had been habituated to, suggesting some level of vigilance. However, this response was too weak to conclude about dishabituation. The results of the study confirm sound recognition and the fast and persistent audio habituation in horses.

FILOSOFI RIDEO ERGO SUM: MEMAKNAI MANUSIA SEBAGAI MAKHLUK YANG TERTAWA (HOMO RIDENS)

The phrase rideo ergo sum or I laugh because I exist is a philosophy that has a very deep meaning. This is because only humans are able to laugh or are referred to as homo ridens while animals cannot. Although there are animal sounds that resemble laughter, they are not a form of laughter like in humans. Laughter is not a form of meaningless expression. Many things can be obtained by interpreting humans as homo ridens. This research uses library research method with Miles and Huberman analysis pattern. The results in this study are 1). Laughter is a form of authenticity from humans, humans need humor and laughter as an expression of the happiness they experience, humans are unique because humans are able to laugh (homo ridens) as their characteristic. 2). Laughter has a philosophy, because laughing is a form of freedom of expression, and laughter is a form of reality of human freedom. 3). Various things are obtained by humans as homo ridens, especially in the health, so that laughing yoga is a form of actualization of the homo ridens.

Искусственный интеллект как посредник между животным и человеком

The development of technology has changed the position of animals in the modern world in various aspects. However, only the achievements of artificial intelligence in the field of natural languages indicated the possibility of reaching a new level of understanding and relationship with animals. Modern technologies have made it possible to isolate and fi x animal sounds and collect a huge array of audio and video data, and the experience of translation, even in the absence of parallel texts, has indicated the potential for using artificial intelligence to analyze animal sounds. Despite numerous difficulties, including those associated with the difference in the worldview of animals and humans, there are already precedents for translation from the language of animals. The article analyzes the possibilities of using artificial intelligence in conditions of limited data and its current use in the field of animal communication. If for domestic and farm animals, researchers rely on the interpretation of meanings or emotions, then for wild animals, scientists compare sounds and behavior, and rely on the potential of artificial intelligence in solving unstructured problems. Although a number of recent studies report high reliability of “translation” from the language of animals, the very possibility of testing the effectiveness is difficult. Nevertheless, the accelerating emergence of new solutions that facilitate the recognition of the voices of specific animals, the classification of sounds and actions of different animals, etc., indicate the possibility of a qualitative leap in the understanding of animals in the near future. Success in the field of interpretation of animal sounds can lead not only to progress in a large number of areas related to the animal world, but also to a change in the status and position of the animal. At the same time, the achievements raise ethical questions related to the possibility of using new technologies to the detriment of animals and people.

Bioacoustic classification of a small dataset of mammalian vocalisations using deep learning

ABSTRACT In the last few years, automatic extraction and classification of animal vocalisations has been facilitated by machine learning (ML) and deep learning (DL) methods. Different frameworks allowed researchers to automatically extract features and perform classification tasks, aiding in call identification and species recognition. However, the success of these applications relies on the amount of available data to train these algorithms. The lack of sufficient data can also lead to overfitting and affect generalisation (i.e. poor performance on out-of-sample data). Further, acquiring large data sets is costly and annotating them is time consuming. Thus, how small can a dataset be to still provide useful information by means of ML or DL? Here, we show how convolutional neural network architectures can handle small datasets in a bioacoustic classification task of affective mammalian vocalisations. We explain how these techniques can be used (e.g. pre-training and data augmentation), and emphasise how to implement them in concordance with features of bioacoustic signals. We further discuss whether these networks can generalise the affective quality of vocalisations across different taxa.

The content of reindeer male vocalisations: acoustic cues to age and size

ABSTRACT Some acoustic parameters of animal vocalisations have been shown to reliably indicate male quality and play a role in mate and rival assessment. Reindeer possess a peculiar vocal tract anatomy involving a laryngeal air sac which probably acts as an additional filter, making it a candidate species for novel investigations in the field of bioacoustics. We investigated whether some acoustic parameters of male rutting vocalisations were good indicators of age and body weight (used as an index for body size). We did this by performing acoustic analyses using recordings collected from a semi-domesticated reindeer population in northern Finland. We found the age of subadult males (aged 2.5–4.5 years) to be negatively correlated with formant F3 and formant spacing, suggesting that their vocalisations convey information on the caller’s age. Individual formant frequencies were not affected by male body weight, but formant spacing was lower in heavier males. Despite the presence of the laryngeal air sac, formant spacing seems to be an acoustic parameter influencing mate and rival assessment in reindeer as it gives an honest indication of male body size. We discuss the importance of reliable acoustic cues to quality indices in sexual selection contexts.

Temporal Coherence Shapes Cortical Responses to Speech Mixtures in a Ferret Cocktail Party.

Segregation of complex sounds such as speech, music and animal vocalizations as they simultaneously emanate from multiple sources (referred to as the "cocktail party problem") is a remarkable ability that is common in humans and animals alike. The neural underpinnings of this process have been extensively studied behaviorally and physiologically in non-human animals primarily with simplified sounds (tones and noise sequences). In humans, segregation experiments utilizing more complex speech mixtures are common; but physiological experiments have relied on EEG/MEG/ECoG recordings that sample activity from thousands of neurons, often obscuring the detailed processes that give rise to the observed segregation. The present study combines the insights from animal single-unit physiology with segregation of speech-like mixtures. Ferrets were trained to attend to a female voice and detect a target word, both in presence or absence of a concurrent, equally salient male voice. Single neuron recordings were obtained from primary and secondary ferret auditory cortical fields, as well as frontal cortex. During task performance, representation of the female words became more enhanced relative to those of the (distractor) male in all cortical regions, especially in the higher auditory cortical field. Analysis of the temporal and spectral response characteristics during task performance reveals how speech segregation gradually emerges in the auditory cortex. A computational model evaluated on the same voice mixtures replicates and extends these results to different attentional targets (attention to female or male voices). These findings are consistent with the temporal coherence theory whereby attention to a target voice anchors neural activity in cortical networks hence binding together channels that are coherently temporally-modulated with the target, and ultimately forming a common auditory stream.

Revised clusters of annotated unknown sounds in the Belgian part of the North sea

Acoustic signals, especially those of biological source, remain unexplored in the Belgian part of the North Sea (BPNS). The BPNS, although dominated by anthrophony (sounds from human activities), is expected to be acoustically diverse given the presence of biodiverse sandbanks, gravel beds and artificial hard structures. Under the framework of the LifeWatch Broadband Acoustic Network, sound data have been collected since the spring of 2020. These recordings, encompassing both biophony, geophony and anthrophony, have been listened to and annotated for unknown, acoustically salient sounds. To obtain the acoustic features of these annotations, we used two existing automatic feature extractions: the Animal Vocalization Encoder based on Self-Supervision (AVES) and a convolutional autoencoder network (CAE) retrained on the data from this study. An unsupervised density-based clustering algorithm (HDBSCAN) was applied to predict clusters. We coded a grid search function to reduce the dimensionality of the feature sets and to adjust the hyperparameters of HDBSCAN. We searched the hyperparameter space for the most optimized combination of parameter values based on two selected clustering evaluation measures: the homogeneity and the density-based clustering validation (DBCV) scores. Although both feature sets produced meaningful clusters, AVES feature sets resulted in more solid, homogeneous clusters with relatively lower intra-cluster distances, appearing to be more advantageous for the purpose and dataset of this study. The 26 final clusters we obtained were revised by a bioacoustics expert. We were able to name and describe 10 unique sounds, but only clusters named as ‘Jackhammer’ and ‘Tick’ can be interpreted as biological with certainty. Although unsupervised clustering is conventional in ecological research, we highlight its practical use in revising clusters of annotated unknown sounds. The revised clusters we detailed in this study already define a few groups of distinct and recurring sounds that could serve as a preliminary component of a valid annotated training dataset potentially feeding supervised machine learning and classifier models.

Using acoustic cameras to study vocal mobbing reveals the importance of learning in juvenile Arabian babblers

IntroductionWhen studying bird intra- and inter-specific interactions it is crucial to accurately track which individual emits which vocalization. However, locating sounds of free moving birds (and other animals) in nature can be challenging, especially in situations when many individuals call in spatial and temporal vicinity.MethodsIn this paper, we will introduce the use of a hand-held acoustic camera which allows solving this problem. The system comprises 64 microphones each sampling a wide angle at a rate of 200kHz. The system can localize up to dozens of nearby callers and it displays them on a visual image. We applied the system to localize and record vocalizations of Arabian babblers (Argya squamiceps) during snake-mobbing behavior to demonstrate its potential. As the recorded babblers are ringed and therefore identifiable, we could use the calling order to analyze the vocalizing mob and to reconstruct a vocal social network depicting which individuals call after which individuals.ResultsAcoustic analysis revealed that Arabian babblers call in a periodic pattern and with age specific pause lengths between different individuals. Mobbing events with young babblers display a lower vocal network density compared to only adult groups.DiscussionOur approach demonstrates how this novel technology can be applied to reveal new insight about vocal events in group living wild animals. The approach can be elaborated to study intra- and inter-specific animal vocalizations.

Where's Whaledo: A software toolkit for array localization of animal vocalizations.

Where's Whaledo is a software toolkit that uses a combination of automated processes and user interfaces to greatly accelerate the process of reconstructing animal tracks from arrays of passive acoustic recording devices. Passive acoustic localization is a non-invasive yet powerful way to contribute to species conservation. By tracking animals through their acoustic signals, important information on diving patterns, movement behavior, habitat use, and feeding dynamics can be obtained. This method is useful for helping to understand habitat use, observe behavioral responses to noise, and develop potential mitigation strategies. Animal tracking using passive acoustic localization requires an acoustic array to detect signals of interest, associate detections on various receivers, and estimate the most likely source location by using the time difference of arrival (TDOA) of sounds on multiple receivers. Where's Whaledo combines data from two small-aperture volumetric arrays and a variable number of individual receivers. In a case study conducted in the Tanner Basin off Southern California, we demonstrate the effectiveness of Where's Whaledo in localizing groups of Ziphius cavirostris. We reconstruct the tracks of six individual animals vocalizing concurrently and identify Ziphius cavirostris tracks despite being obscured by a large pod of vocalizing dolphins.

A scoping review of the use of bioacoustics to assess various components of farm animal welfare

Recording and analyzing animal sounds, including vocal and non-vocal cues, is a relevant tool to access and monitor different aspects of animal physiology, behaviour and general welfare in a remote, non-invasive and continuous fashion. This field, known as bioacoustics, is considerably growing, notably due to the development of machine learning techniques. Yet, applications of these methods in animal husbandry remain limited, and their potential may not be fully acknowledged. This review aims to present the potential of bioacoustic tools to monitor various aspects of the welfare of farm animals, and focuses on studies published since 2010. The first section of the review introduces the concept of bioacoustics, describes the different parameters that can be extracted from the sounds of animals, and explains how they can be analysed. The potential of these measures to inform about the physical characteristics, individuality, health, physiology, behaviour, and affective states of different species of farm animals are then presented, through a contemporary overview of the research field. Although this analysis identifies a variety of applications of bioacoustics tools in relation to the assessment and monitoring of farm animal welfare, it also points out the current scarcity of methods implemented in practice and available to farm management. Barriers to the development of bioacoustics measures in conventional farming are also discussed, and future directions for this field of research are suggested.

Sound identification of abnormal pig vocalizations: Enhancing livestock welfare monitoring on smart farms

In smart agriculture, analyzing animal vocalizations provides a non-invasive, continuous monitoring approach that directly correlates with specific animal conditions, enhancing welfare. Pig vocalizations, in particular, are critical for managing farm events and improving animal welfare. Yet, traditional methods, mainly involving Convolutional Neural Networks (CNNs), focus on local audio features and complex combinations, struggling with varying audio lengths and high computational costs.Addressing these issues, this study introduces a novel approach with an Audio Spectrogram Transformer (AST), designed to detect abnormal pig vocalizations. Our method involves a two-stage process: segmenting the audio to retain only valuable information, and classifying through an attention mechanism that analyzes both nuanced and global audio features. This technique significantly improves accuracy and efficiency in vocalization analysis.Tested on 7600 real-world audio samples, our method demonstrated a significant improvement, achieving an accuracy of 93 % and enhancing inference speed by 19 times compared to existing CNN-based techniques. Additionally, we conducted interpretability analysis and feature selection experiments to evaluate the efficacy of different feature combinations. These experiments verified that our attention-based approach not only simplifies the input features but also provides superior performance over traditional models. The findings of this study underscore the potential of AST in transforming livestock welfare monitoring by offering a more accurate, efficient, and scalable solution.

Deep learning in marine bioacoustics: a benchmark for baleen whale detection

AbstractPassive acoustic monitoring (PAM) is commonly used to obtain year‐round continuous data on marine soundscapes harboring valuable information on species distributions or ecosystem dynamics. This continuously increasing amount of data requires highly efficient automated analysis techniques in order to exploit the full potential of the available data. Here, we propose a benchmark, which consists of a public dataset, a well‐defined task and evaluation procedure to develop and test automated analysis techniques. This benchmark focuses on the special case of detecting animal vocalizations in a real‐world dataset from the marine realm. We believe that such a benchmark is necessary to monitor the progress in the development of new detection algorithms in the field of marine bioacoustics. We ultimately use the proposed benchmark to test three detection approaches, namely ANIMAL‐SPOT, Koogu and a simple custom sequential convolutional neural network (CNN), and report performances. We report the performance of the three detection approaches in a blocked cross‐validation fashion with 11 site‐year blocks for a multi‐species detection scenario in a large marine passive acoustic dataset. Performance was measured with three simple metrics (i.e., true classification rate, noise misclassification rate and call misclassification rate) and one combined fitness metric, which allocates more weight to the minimization of false positives created by noise. Overall, ANIMAL‐SPOT performed the best with an average F metric of 0.83, followed by the custom CNN with an average fitness metric of 0.79 and finally Koogu with an average fitness metric of 0.59. The presented benchmark is an important step to advance in the automatic processing of the continuously growing amount of PAM data that are collected throughout the world's oceans. To ultimately achieve usability of developed algorithms, the focus of future work should be laid on the reduction of the false positives created by noise.

Field tests of small autonomous recording units: an evaluation of in-person versus automated point counts and a comparison of recording quality

ABSTRACT The proliferation of small autonomous recorders makes it easier than ever to sample terrestrial acoustic animals and soundscapes. I conducted a comparison of four small recorders to evaluate their performance in a field setting: Wildlife Acoustics Song Meter Mini; Wildlife Acoustics Song Meter Micro; Open Acoustics Audiomoth; and Cornell SwiftOne. I address two questions: (1) How do in-person point counts compare to recorder-based point counts using these small autonomous recorders? (2) How does the quality of the recordings compare across these small autonomous recorders? To evaluate the performance of the recorders in point counts, I conducted in-person and recording-based point counts at ten locations. Each of the recorders performed similarly well at point counts, producing comparable estimates of species richness, although all of the autonomous recorders under-estimated species richness. To evaluate recording quality, I conducted a sound transmission test, broadcasting and re-recording sounds. Recorders varied in their frequency response above 12 kHz, but showed only subtle differences in the frequency response at frequencies below 12 kHz. I conclude that each of these types of small recorders provide bioacousticians with useful tools for conducting point counts, and for passive monitoring of animal sounds, with only subtle differences across the investigated models.

Particle motion polarization of offshore fish vocalizations, boat noise, and ambient soundscapes

Acoustic particle motion is an important acoustic cue for fish and aquatic invertebrate hearing. When reported for bioacoustics applications, it is often described as a magnitude only. However, particle motion is a vector quantity with polarization information (including directionality) relevant to detection and localization of sound cues by fishes and invertebrates. This study applied established metrics of particle motion polarization including ellipse amplitude, angle, orientation, and degree of polarization, to compare sounds of fish vocalizations with that of boat and ambient sounds at a 900 m-deep site (in the Atlantic Deepwater Ecosystem Observatory Network) offshore of Florida. Polarization metrics were computed for bivariate signals in the plane formed by the horizontal source-receiver and vertical axes. Boat sound polarization was quantified at multiple distances from the closest point of approach. Fish and boat sounds had narrower particle motion ellipses and larger amplitudes in the horizontal compared to ambient sounds that were more circular with larger relative vertical amplitudes. Fish and boat sounds were often strongly polarized, which may allow spatial release from masking given sufficient angular separation of sounds. This analysis framework has promising applications for monitoring in situ directionality of fish and other animal vocalizations and for modelling directional masking.

Detecting tail biters by monitoring pig screams in weaning pigs

Early identification of tail biting and intervention are necessary to reduce tail lesions and their impact on animal health and welfare. Removal of biters has become an effective intervention strategy, but finding them can be difficult and time-consuming. The aim of this study was to investigate whether tail biting and, in particular, individual biters could be identified by detecting pig screams in audio recordings. The study included 288 undocked weaner pigs housed in six pens in two batches. Once a tail biter (n = 7) was identified by visual inspection in the stable and removed by the farm staff, the previous days of video and audio recordings were analyzed for pig screams (sudden increase in loudness with frequencies above 1 kHz) and tail biting events until no biting before the removal was observed anymore. In total, 2893 screams were detected in four pens where tail biting occurred. Of these screams, 52.9% were caused by tail biting in the observed pen, 25.6% originated from other pens, 8.8% were not assignable, and 12.7% occurred due to other reasons. In case of a tail biting event, screams were assigned individually to biter and victim pigs. Based on the audio analysis, biters were identified between one and nine days prior to their removal from the pen after visual inspection. Screams were detected earlier than the increase in hanging tails and could therefore be favored as an early warning indicator. Analyzing animal vocalization has potential for monitoring and early detection of tail biting events. In combination with individual marks and automatic analysis algorithms, biters could be identified and tail biting efficiently reduced. In this way, biters can be removed earlier to increase animal health and welfare.

Calls of the little auk (Alle alle) chicks reflect their behavioural contexts.

Animal vocalisations can often inform conspecifics about the behavioural context of production and the underlying affective states, hence revealing whether a situation should be approached or avoided. While this is particularly important for socially complex species, little is known about affective expression in wild colonial animals, and even less to about their young. We studied vocalisations of the little auk (Alle alle) chicks in the Hornsund breeding colony, Svalbard. Little auks are highly colonial seabirds, and adults convey complex behavioural contexts through their calls. We recorded chick calls during two contexts of opposite affective valence: handing by a human, and while they interact with their parents inside the nest. Using permuted discriminant function analysis and a series of linear mixed models, we examined the effect of the production context/associated affective valence on the acoustic parameters of those calls. Calls were reliably classified to their context, with over 97% accuracy. Calls uttered during handling had higher mean entropy, fundamental frequency, as well as lower spectral centre of gravity and a less steep spectral slope compared to calls produced during interactions with a parent inside the nest. The individuality of handling calls, assessed by information content, was lower than the individuality of calls uttered in the nest. These findings suggest that seabird chicks can effectively communicate behavioural/affective contexts through calls, conveying socially important messages early in development. Our results are mostly in line with emotional expression patterns observed across taxa, supporting their evolutionary continuity.

A Task-Adaptive Parameter Transformation Scheme for Model-Agnostic-Meta-Learning-Based Few-Shot Animal Sound Classification

Deep learning models that require vast amounts of training data struggle to achieve good animal sound classification (ASC) performance. Among recent few-shot ASC methods to address the data shortage problem regarding animals that are difficult to observe, model-agnostic meta-learning (MAML) has shown new possibilities by encoding common prior knowledge derived from different tasks into the model parameter initialization of target tasks. However, when the knowledge on animal sounds is difficult to generalize due to its diversity, MAML exhibits poor ASC performance due to the static initialization setting. In this paper, we propose a novel task-adaptive parameter transformation scheme called few-shot ASC. TAPT generates transformation variables while learning common knowledge and uses the variables to make parameters specific to the target task. Owing to this transformation, TAPT can reduce overfitting and enhance adaptability, training speed, and performance in heterogeneous tasks compared to MAML. In experiments on two public datasets on the same backbone network, we show that TAPT outperforms the existing few-shot ASC schemes in terms of classification accuracy, and in particular a performance improvement of 20.32% compared to the state-of-the-art scheme. In addition, we show that TAPT is robust to hyperparameters and efficient for training.

Small apes adjust rhythms to facilitate song coordination

Song coordination is a universal characteristic of human music. Many animals also produce well-coordinated duets or choruses that resemble human music. However, the mechanism and evolution of song coordination have only recently been studied in animals. Here, we studied the mechanism of song coordination in three closely related species of wild Nomascus gibbons that live in polygynous groups. In each species, song bouts were dominated by male solo sequences (referred to hereafter as male sequence), and females contributed stereotyped great calls to coordinate with males. Considering the function of rhythm in facilitating song coordination in human music and animal vocalizations, we predicted that adult males adjust their song rhythm to facilitate song coordination with females. In support of this prediction, we found that adult males produced significantly more isochronous rhythms with a faster tempo in male sequences that were followed by successful female great calls (a complete sequence with "introductory" and "wa" notes). The difference in isochrony and tempos between successful great call sequences and male sequences was smaller in N.concolor compared with the other two species, which may make it difficult for females to predict a male's precise temporal pattern. Consequently, adult females of N.concolor produced more failed great call (an incomplete sequence with only introductory notes) sequences. We propose that the high degree of rhythm change functions as an unambiguous signal that can be easily perceived by receivers. In this regard, gibbon vocalizations offer an instructive model to understand the origins and evolution of human music.

Plant Bioacoustics: A system of plant-sound relationship

Bioacoustics is a field of study that examines the production of sound and how it affects living things. Numerous plant species' physiology, behaviour, and eventual survival have all been greatly influenced by sound and its usage in communication. A better framework for future research may be developed along with a greater understanding of how various organisms interact acoustically with plants if the acoustic link between plants and animals is understood. A re-imagination of our knowledge of these organisms is anticipated to result from the systematic investigation of the functional and evolutionary importance of sound in plant life. This will also stimulate the emergence of new ideas and viewpoints regarding the communicative complexity of plants. The primary goal of this study is to examine some information about the bioacoustics interaction between plants and animals their sound, and ecology, including potential techniques of sound production employed by plants.
 The importance of acoustical research in plant ecology, as well as its potential mechanisms and future applications, are covered in this paper. The first section of this article reviews how plants amplify and transmit sounds produced by insect pests. The second section looks at surprising examples of carnivorous plants that show how plants have evolved to reflect but also enhance animal sounds, potentially revealing new angles in research on the interactions between animals and plants. The discussion then focuses on the mechanisms by which plants produce sound through transpiration stress and photosynthesis, as well as a potential model for these mechanisms.

Rancang Bangun Aplikasi Smart Kids English Berbasis Mobile

Smart Kids English is a mobile-based learning media application that aims to help teachers and parents accompany children in learning English with different experiences by utilizing technology. The use of learning software is considered more effective because basically children use gadgets more often in their daily activities. Using learning software on gadget devices can also minimize the use of gadgets for unimportant applications such as playing games. The System Development Life Cycle in this research uses the Waterfall method, this method is used by many software developers. This research produces the Smart Kids English application with several basic features such as: pronunciation which is equipped with attractive images. Smart Kids English is equipped with a writing feature to train children in writing English. Smart Kids English is equipped with an animal sounds feature to increase children's insight into recognizing animal sounds in the environment around us. Smart Kids English is also equipped with a practice menu, the aim of which is to sharpen children's memory in remembering the material they have studied.